Conventionally, there are various methods for the extraction of moving vectors for indicating the direction and speed of movement of an image. The direction and speed is calculated by dividing a given image into blocks of a predetermined size and by calculating a moving vector according to the relationship between successive image frames for each block. Such methods include a gradient method concerning spatial and time gradient of brightness, a phase correlation method in which phase terms ratio of coefficients of Fourier transformation is used, and a representative point matching method in which the accumulated value of the absolute value of differences between frames at representative points in successive images is used. The "representative point matching method" used in a MUSE encoder is advantageous because the size of hardware is smaller than that for the other above-mentioned methods.
For a representative point matching method, for example, a frame image may be divided into four blocks, as shown in FIG. 2, and a moving vector of one of those blocks is calculated. First, a number of representative points, equal to b.times.c=P, are. The absolute value of the difference between frames is generated by the following formula, using multi-values in a searching area of m pixels.times.n pixels. EQU P.sub.d,e (i, j)=.vertline.a.sup.n.sub.d,e (i, j)-a.sup.n-1.sub.d,e (0,0).vertline.
where a.sup.n .sub.d,e (i,j) is a current image frame and a.sup.n-1.sub.d,e (0,0) is a previous image frame. For each representative point, the sum of the accumulated value of P.sub.d,e (i, j) is calculated by a following formula ##EQU1## and the displacement value (i, j) that has the minimum value is considered as a moving vector. This is the representative matching method. FIG. 15 describes the concept. As will be understood from this figure, by calculating the absolute value of the difference between frames (mark I), a data for the absolute value of the difference including a curved line K whose value is 0 (mark J) will be obtained. By accumulating this data for the absolute value of the difference for each representative point, the accumulated value at the intersection of the curved line K will be the minimum value, and this is the position of the moving vector M. The accumulation function P(i, j) will be a funnel-like concavity centering the moving vector position (i, j).